metadata

tags:
  - tensorflowtts
  - audio
  - text-to-speech
  - text-to-mel
language: eng
license: apache-2.0
datasets:
  - LJSpeech
widget:
  - text: How are you?

FastSpeech2 trained on LJSpeech (Eng)

This repository provides a pretrained FastSpeech2 trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about TensorFlowTTS.

Install TensorFlowTTS

First of all, please install TensorFlowTTS with the following command:

pip install TensorFlowTTS

Converting your Text to Mel Spectrogram

import numpy as np
import soundfile as sf
import yaml

import tensorflow as tf

from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel

processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en")
fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en")

text = "How are you?"

input_ids = processor.text_to_sequence(text)

mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference(
    input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
    speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
    speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
    f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
    energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
)

Referencing FastSpeech2

@misc{ren2021fastspeech,
      title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech}, 
      author={Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu},
      year={2021},
      eprint={2006.04558},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}

Referencing TensorFlowTTS

@misc{TFTTS,
    author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata, 
    Trinh Le and Yunchao He},
    title = {TensorflowTTS},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
  }